Merge remote-tracking branch 'origin/benchmark-fixes-vit_hybrids' into pit_and_vit_update

benchmark.py

@@ -0,0 +1,470 @@
+#!/usr/bin/env python3
+""" Model Benchmark Script
+
+An inference and train step benchmark script for timm models.
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
+"""
+import argparse
+import os
+import csv
+import json
+import time
+import logging
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+from collections import OrderedDict
+from contextlib import suppress
+from functools import partial
+
+from timm.models import create_model, is_model, list_models
+from timm.optim import create_optimizer
+from timm.data import resolve_data_config
+from timm.utils import AverageMeter, setup_default_logging
+
+
+has_apex = False
+try:
+    from apex import amp
+    has_apex = True
+except ImportError:
+    pass
+
+has_native_amp = False
+try:
+    if getattr(torch.cuda.amp, 'autocast') is not None:
+        has_native_amp = True
+except AttributeError:
+    pass
+
+torch.backends.cudnn.benchmark = True
+_logger = logging.getLogger('validate')
+
+
+parser = argparse.ArgumentParser(description='PyTorch Benchmark')
+
+# benchmark specific args
+parser.add_argument('--model-list', metavar='NAME', default='',
+                    help='txt file based list of model names to benchmark')
+parser.add_argument('--bench', default='both', type=str,
+                    help="Benchmark mode. One of 'inference', 'train', 'both'. Defaults to 'inference'")
+parser.add_argument('--detail', action='store_true', default=False,
+                    help='Provide train fwd/bwd/opt breakdown detail if True. Defaults to False')
+parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
+                    help='Output csv file for validation results (summary)')
+
+# common inference / train args
+parser.add_argument('--model', '-m', metavar='NAME', default='resnet50',
+                    help='model architecture (default: resnet50)')
+parser.add_argument('-b', '--batch-size', default=256, type=int,
+                    metavar='N', help='mini-batch size (default: 256)')
+parser.add_argument('--img-size', default=None, type=int,
+                    metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--input-size', default=None, nargs=3, type=int,
+                    metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
+parser.add_argument('--num-classes', type=int, default=None,
+                    help='Number classes in dataset')
+parser.add_argument('--gp', default=None, type=str, metavar='POOL',
+                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
+parser.add_argument('--channels-last', action='store_true', default=False,
+                    help='Use channels_last memory layout')
+parser.add_argument('--amp', action='store_true', default=False,
+                    help='Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.')
+parser.add_argument('--apex-amp', action='store_true', default=False,
+                    help='Use NVIDIA Apex AMP mixed precision')
+parser.add_argument('--native-amp', action='store_true', default=False,
+                    help='Use Native Torch AMP mixed precision')
+parser.add_argument('--torchscript', dest='torchscript', action='store_true',
+                    help='convert model torchscript for inference')
+
+
+# train optimizer parameters
+parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
+                    help='Optimizer (default: "sgd"')
+parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
+                    help='Optimizer Epsilon (default: None, use opt default)')
+parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
+                    help='Optimizer Betas (default: None, use opt default)')
+parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
+                    help='Optimizer momentum (default: 0.9)')
+parser.add_argument('--weight-decay', type=float, default=0.0001,
+                    help='weight decay (default: 0.0001)')
+parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
+                    help='Clip gradient norm (default: None, no clipping)')
+parser.add_argument('--clip-mode', type=str, default='norm',
+                    help='Gradient clipping mode. One of ("norm", "value", "agc")')
+
+
+# model regularization / loss params that impact model or loss fn
+parser.add_argument('--smoothing', type=float, default=0.1,
+                    help='Label smoothing (default: 0.1)')
+parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
+                    help='Dropout rate (default: 0.)')
+parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
+                    help='Drop path rate (default: None)')
+parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
+                    help='Drop block rate (default: None)')
+
+
+def timestamp(sync=False):
+    return time.perf_counter()
+
+
+def cuda_timestamp(sync=False, device=None):
+    if sync:
+        torch.cuda.synchronize(device=device)
+    return time.perf_counter()
+
+
+def count_params(model):
+    return sum([m.numel() for m in model.parameters()])
+
+
+class BenchmarkRunner:
+    def __init__(self, model_name, detail=False, device='cuda', torchscript=False, **kwargs):
+        self.model_name = model_name
+        self.detail = detail
+        self.device = device
+        self.model = create_model(
+            model_name,
+            num_classes=kwargs.pop('num_classes', None),
+            in_chans=3,
+            global_pool=kwargs.pop('gp', 'fast'),
+            scriptable=torchscript).to(device=self.device)
+        self.num_classes = self.model.num_classes
+        self.param_count = count_params(self.model)
+        _logger.info('Model %s created, param count: %d' % (model_name, self.param_count))
+
+        self.channels_last = kwargs.pop('channels_last', False)
+        self.use_amp = kwargs.pop('use_amp', '')
+        self.amp_autocast = torch.cuda.amp.autocast if self.use_amp == 'native' else suppress
+        if torchscript:
+            self.model = torch.jit.script(self.model)
+
+        data_config = resolve_data_config(kwargs, model=self.model, use_test_size=True)
+        self.input_size = data_config['input_size']
+        self.batch_size = kwargs.pop('batch_size', 256)
+
+        self.example_inputs = None
+        self.num_warm_iter = 10
+        self.num_bench_iter = 50
+        self.log_freq = 10
+        if 'cuda' in self.device:
+            self.time_fn = partial(cuda_timestamp, device=self.device)
+        else:
+            self.time_fn = timestamp
+
+    def _init_input(self):
+        self.example_inputs = torch.randn((self.batch_size,) + self.input_size, device=self.device)
+        if self.channels_last:
+            self.example_inputs = self.example_inputs.contiguous(memory_format=torch.channels_last)
+
+
+class InferenceBenchmarkRunner(BenchmarkRunner):
+
+    def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
+        super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
+        self.model.eval()
+        if self.use_amp == 'apex':
+            self.model = amp.initialize(self.model, opt_level='O1')
+        if self.channels_last:
+            self.model = self.model.to(memory_format=torch.channels_last)
+
+    def run(self):
+        def _step():
+            t_step_start = self.time_fn()
+            with self.amp_autocast():
+                output = self.model(self.example_inputs)
+            t_step_end = self.time_fn(True)
+            return t_step_end - t_step_start
+
+        _logger.info(
+            f'Running inference benchmark on {self.model_name} for {self.num_bench_iter} steps w/ '
+            f'input size {self.input_size} and batch size {self.batch_size}.')
+
+        with torch.no_grad():
+            self._init_input()
+
+            for _ in range(self.num_warm_iter):
+                _step()
+
+            total_step = 0.
+            num_samples = 0
+            t_run_start = self.time_fn()
+            for i in range(self.num_bench_iter):
+                delta_fwd = _step()
+                total_step += delta_fwd
+                num_samples += self.batch_size
+                if (i + 1) % self.log_freq == 0:
+                    _logger.info(
+                        f"Infer [{i + 1}/{self.num_bench_iter}]."
+                        f" {num_samples / total_step:0.2f} samples/sec."
+                        f" {1000 * total_step / num_samples:0.3f} ms/sample.")
+            t_run_end = self.time_fn(True)
+            t_run_elapsed = t_run_end - t_run_start
+
+        results = dict(
+            samples_per_sec=round(num_samples / t_run_elapsed, 2),
+            step_time=round(1000 * total_step / num_samples, 3),
+            batch_size=self.batch_size,
+            img_size=self.input_size[-1],
+            param_count=round(self.param_count / 1e6, 2),
+        )
+
+        _logger.info(
+            f"Inference benchmark of {self.model_name} done. "
+            f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/sample")
+
+        return results
+
+
+class TrainBenchmarkRunner(BenchmarkRunner):
+
+    def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
+        super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
+        self.model.train()
+
+        if kwargs.pop('smoothing', 0) > 0:
+            self.loss = nn.CrossEntropyLoss().to(self.device)
+        else:
+            self.loss = nn.CrossEntropyLoss().to(self.device)
+        self.target_shape = tuple()
+
+        self.optimizer = create_optimizer(
+            self.model,
+            opt_name=kwargs.pop('opt', 'sgd'),
+            lr=kwargs.pop('lr', 1e-4))
+
+        if self.use_amp == 'apex':
+            self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level='O1')
+        if self.channels_last:
+            self.model = self.model.to(memory_format=torch.channels_last)
+
+    def _gen_target(self, batch_size):
+        return torch.empty(
+            (batch_size,) + self.target_shape, device=self.device, dtype=torch.long).random_(self.num_classes)
+
+    def run(self):
+        def _step(detail=False):
+            self.optimizer.zero_grad()  # can this be ignored?
+            t_start = self.time_fn()
+            t_fwd_end = t_start
+            t_bwd_end = t_start
+            with self.amp_autocast():
+                output = self.model(self.example_inputs)
+                if isinstance(output, tuple):
+                    output = output[0]
+                if detail:
+                    t_fwd_end = self.time_fn(True)
+                target = self._gen_target(output.shape[0])
+                self.loss(output, target).backward()
+                if detail:
+                    t_bwd_end = self.time_fn(True)
+            self.optimizer.step()
+            t_end = self.time_fn(True)
+            if detail:
+                delta_fwd = t_fwd_end - t_start
+                delta_bwd = t_bwd_end - t_fwd_end
+                delta_opt = t_end - t_bwd_end
+                return delta_fwd, delta_bwd, delta_opt
+            else:
+                delta_step = t_end - t_start
+                return delta_step
+
+        _logger.info(
+            f'Running train benchmark on {self.model_name} for {self.num_bench_iter} steps w/ '
+            f'input size {self.input_size} and batch size {self.batch_size}.')
+
+        self._init_input()
+
+        for _ in range(self.num_warm_iter):
+            _step()
+
+        t_run_start = self.time_fn()
+        if self.detail:
+            total_fwd = 0.
+            total_bwd = 0.
+            total_opt = 0.
+            num_samples = 0
+            for i in range(self.num_bench_iter):
+                delta_fwd, delta_bwd, delta_opt = _step(True)
+                num_samples += self.batch_size
+                total_fwd += delta_fwd
+                total_bwd += delta_bwd
+                total_opt += delta_opt
+                if (i + 1) % self.log_freq == 0:
+                    total_step = total_fwd + total_bwd + total_opt
+                    _logger.info(
+                        f"Train [{i + 1}/{self.num_bench_iter}]."
+                        f" {num_samples / total_step:0.2f} samples/sec."
+                        f" {1000 * total_fwd / num_samples:0.3f} ms/sample fwd,"
+                        f" {1000 * total_bwd / num_samples:0.3f} ms/sample bwd,"
+                        f" {1000 * total_opt / num_samples:0.3f} ms/sample opt."
+                    )
+            total_step = total_fwd + total_bwd + total_opt
+            t_run_elapsed = self.time_fn() - t_run_start
+            results = dict(
+                samples_per_sec=round(num_samples / t_run_elapsed, 2),
+                step_time=round(1000 * total_step / num_samples, 3),
+                fwd_time=round(1000 * total_fwd / num_samples, 3),
+                bwd_time=round(1000 * total_bwd / num_samples, 3),
+                opt_time=round(1000 * total_opt / num_samples, 3),
+                batch_size=self.batch_size,
+                img_size=self.input_size[-1],
+                param_count=round(self.param_count / 1e6, 2),
+            )
+        else:
+            total_step = 0.
+            num_samples = 0
+            for i in range(self.num_bench_iter):
+                delta_step = _step(False)
+                num_samples += self.batch_size
+                total_step += delta_step
+                if (i + 1) % self.log_freq == 0:
+                    _logger.info(
+                        f"Train [{i + 1}/{self.num_bench_iter}]."
+                        f" {num_samples / total_step:0.2f} samples/sec."
+                        f" {1000 * total_step / num_samples:0.3f} ms/sample.")
+            t_run_elapsed = self.time_fn() - t_run_start
+            results = dict(
+                samples_per_sec=round(num_samples / t_run_elapsed, 2),
+                step_time=round(1000 * total_step / num_samples, 3),
+                batch_size=self.batch_size,
+                param_count=round(self.param_count / 1e6, 2),
+            )
+
+        _logger.info(
+            f"Train benchmark of {self.model_name} done. "
+            f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/sample")
+
+        return results
+
+
+def decay_batch_exp(batch_size, factor=0.5, divisor=16):
+    out_batch_size = batch_size * factor
+    if out_batch_size > divisor:
+        out_batch_size = (out_batch_size + 1) // divisor * divisor
+    else:
+        out_batch_size = batch_size - 1
+    return max(0, int(out_batch_size))
+
+
+def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
+    batch_size = initial_batch_size
+    results = dict()
+    while batch_size >= 1:
+        try:
+            bench = bench_fn(model_name=model_name, batch_size=batch_size, **bench_kwargs)
+            results = bench.run()
+            return results
+        except RuntimeError as e:
+            torch.cuda.empty_cache()
+            batch_size = decay_batch_exp(batch_size)
+            print(f'Error: {str(e)} while running benchmark. Reducing batch size to {batch_size} for retry.')
+    return results
+
+
+def benchmark(args):
+    if args.amp:
+        if has_native_amp:
+            args.native_amp = True
+        elif has_apex:
+            args.apex_amp = True
+        else:
+            _logger.warning("Neither APEX or Native Torch AMP is available.")
+    if args.native_amp:
+        args.use_amp = 'native'
+        _logger.info('Benchmarking in mixed precision with native PyTorch AMP.')
+    elif args.apex_amp:
+        args.use_amp = 'apex'
+        _logger.info('Benchmarking in mixed precision with NVIDIA APEX AMP.')
+    else:
+        args.use_amp = ''
+        _logger.info('Benchmarking in float32. AMP not enabled.')
+
+    bench_kwargs = vars(args).copy()
+    model = bench_kwargs.pop('model')
+    batch_size = bench_kwargs.pop('batch_size')
+
+    bench_fns = (InferenceBenchmarkRunner,)
+    prefixes = ('infer',)
+    if args.bench == 'both':
+        bench_fns = (
+            InferenceBenchmarkRunner,
+            TrainBenchmarkRunner
+        )
+        prefixes = ('infer', 'train')
+    elif args.bench == 'train':
+        bench_fns = TrainBenchmarkRunner,
+        prefixes = 'train',
+
+    model_results = OrderedDict(model=model)
+    for prefix, bench_fn in zip(prefixes, bench_fns):
+        run_results = _try_run(model, bench_fn, initial_batch_size=batch_size, bench_kwargs=bench_kwargs)
+        if prefix:
+            run_results = {'_'.join([prefix, k]): v for k, v in run_results.items()}
+        model_results.update(run_results)
+    param_count = model_results.pop('infer_param_count', model_results.pop('train_param_count', 0))
+    model_results.setdefault('param_count', param_count)
+    model_results.pop('train_param_count', 0)
+    return model_results
+
+
+def main():
+    setup_default_logging()
+    args = parser.parse_args()
+    model_cfgs = []
+    model_names = []
+
+    if args.model_list:
+        args.model = ''
+        with open(args.model_list) as f:
+            model_names = [line.rstrip() for line in f]
+        model_cfgs = [(n, None) for n in model_names]
+    elif args.model == 'all':
+        # validate all models in a list of names with pretrained checkpoints
+        args.pretrained = True
+        model_names = list_models(pretrained=True, exclude_filters=['*in21k'])
+        model_cfgs = [(n, None) for n in model_names]
+    elif not is_model(args.model):
+        # model name doesn't exist, try as wildcard filter
+        model_names = list_models(args.model)
+        model_cfgs = [(n, None) for n in model_names]
+
+    if len(model_cfgs):
+        results_file = args.results_file or './benchmark.csv'
+        _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
+        results = []
+        try:
+            for m, _ in model_cfgs:
+                if not m:
+                    continue
+                args.model = m
+                r = benchmark(args)
+                results.append(r)
+        except KeyboardInterrupt as e:
+            pass
+        sort_key = 'train_samples_per_sec' if 'train' in args.bench else 'infer_samples_per_sec'
+        results = sorted(results, key=lambda x: x[sort_key], reverse=True)
+        if len(results):
+            write_results(results_file, results)
+
+        import json
+        json_str = json.dumps(results, indent=4)
+        print(json_str)
+    else:
+        benchmark(args)
+
+
+def write_results(results_file, results):
+    with open(results_file, mode='w') as cf:
+        dw = csv.DictWriter(cf, fieldnames=results[0].keys())
+        dw.writeheader()
+        for r in results:
+            dw.writerow(r)
+        cf.flush()
+
+
+if __name__ == '__main__':
+    main()

timm/data/config.py

@@ -5,7 +5,7 @@ from .constants import *
 _logger = logging.getLogger(__name__)
 
 
-def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=True):
+def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=False):
     new_config = {}
     default_cfg = default_cfg
     if not default_cfg and model is not None and hasattr(model, 'default_cfg'):

timm/data/dataset.py

@@ -73,12 +73,13 @@ class IterableImageDataset(data.IterableDataset):
             batch_size=None,
             class_map='',
             load_bytes=False,
+            repeats=0,
             transform=None,
     ):
         assert parser is not None
         if isinstance(parser, str):
             self.parser = create_parser(
-                parser, root=root, split=split, is_training=is_training, batch_size=batch_size)
+                parser, root=root, split=split, is_training=is_training, batch_size=batch_size, repeats=repeats)
         else:
             self.parser = parser
         self.transform = transform

timm/data/dataset_factory.py

@@ -23,6 +23,7 @@ def create_dataset(name, root, split='validation', search_split=True, is_trainin
             root, parser=name, split=split, is_training=is_training, batch_size=batch_size, **kwargs)
     else:
         # FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
+        kwargs.pop('repeats', 0)  # FIXME currently only Iterable dataset support the repeat multiplier
         if search_split and os.path.isdir(root):
             root = _search_split(root, split)
         ds = ImageDataset(root, parser=name, **kwargs)

timm/data/parsers/parser_tfds.py

@@ -29,6 +29,11 @@ SHUFFLE_SIZE = 16834  # samples to shuffle in DS queue
 PREFETCH_SIZE = 4096  # samples to prefetch
 
 
+def even_split_indices(split, n, num_samples):
+    partitions = [round(i * num_samples / n) for i in range(n + 1)]
+    return [f"{split}[{partitions[i]}:{partitions[i+1]}]" for i in range(n)]
+
+
 class ParserTfds(Parser):
     """ Wrap Tensorflow Datasets for use in PyTorch
 
@@ -52,7 +57,7 @@ class ParserTfds(Parser):
         components.
 
     """
-    def __init__(self, root, name, split='train', shuffle=False, is_training=False, batch_size=None):
+    def __init__(self, root, name, split='train', shuffle=False, is_training=False, batch_size=None, repeats=0):
         super().__init__()
         self.root = root
         self.split = split
@@ -62,6 +67,8 @@ class ParserTfds(Parser):
             assert batch_size is not None,\
                 "Must specify batch_size in training mode for reasonable behaviour w/ TFDS wrapper"
         self.batch_size = batch_size
+        self.repeats = repeats
+        self.subsplit = None
 
         self.builder = tfds.builder(name, data_dir=root)
         # NOTE: please use tfds command line app to download & prepare datasets, I don't want to call
@@ -95,6 +102,7 @@ class ParserTfds(Parser):
         if worker_info is not None:
             self.worker_info = worker_info
             num_workers = worker_info.num_workers
+            global_num_workers = self.dist_num_replicas * num_workers
             worker_id = worker_info.id
 
             # FIXME I need to spend more time figuring out the best way to distribute/split data across
@@ -114,19 +122,31 @@ class ParserTfds(Parser):
             #     split = split + '[{}:]'.format(start)
             # else:
             #     split = split + '[{}:{}]'.format(start, start + split_size)
+            if not self.is_training and '[' not in self.split:
+                # If not training, and split doesn't define a subsplit, manually split the dataset
+                # for more even samples / worker
+                self.subsplit = even_split_indices(self.split, global_num_workers, self.num_samples)[
+                    self.dist_rank * num_workers + worker_id]
 
-        input_context = tf.distribute.InputContext(
+        if self.subsplit is None:
-            num_input_pipelines=self.dist_num_replicas * num_workers,
+            input_context = tf.distribute.InputContext(
-            input_pipeline_id=self.dist_rank * num_workers + worker_id,
+                num_input_pipelines=self.dist_num_replicas * num_workers,
-            num_replicas_in_sync=self.dist_num_replicas  # FIXME does this have any impact?
+                input_pipeline_id=self.dist_rank * num_workers + worker_id,
-        )
+                num_replicas_in_sync=self.dist_num_replicas  # FIXME does this arg have any impact?
+            )
+        else:
+            input_context = None
 
-        read_config = tfds.ReadConfig(input_context=input_context)
+        read_config = tfds.ReadConfig(
-        ds = self.builder.as_dataset(split=split, shuffle_files=self.shuffle, read_config=read_config)
+            shuffle_seed=42,
+            shuffle_reshuffle_each_iteration=True,
+            input_context=input_context)
+        ds = self.builder.as_dataset(
+            split=self.subsplit or self.split, shuffle_files=self.shuffle, read_config=read_config)
         # avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
         ds.options().experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
         ds.options().experimental_threading.max_intra_op_parallelism = 1
-        if self.is_training:
+        if self.is_training or self.repeats > 1:
             # to prevent excessive drop_last batch behaviour w/ IterableDatasets
             # see warnings at https://pytorch.org/docs/stable/data.html#multi-process-data-loading
             ds = ds.repeat()  # allow wrap around and break iteration manually
@@ -143,7 +163,7 @@ class ParserTfds(Parser):
         #     This adds extra samples and will slightly alter validation results.
         #   2. determine loop ending condition in training w/ repeat enabled so that only full batch_size
         #     batches are produced (underlying tfds iter wraps around)
-        target_sample_count = math.ceil(self.num_samples / self._num_pipelines)
+        target_sample_count = math.ceil(max(1, self.repeats) * self.num_samples / self._num_pipelines)
         if self.is_training:
             # round up to nearest batch_size per worker-replica
             target_sample_count = math.ceil(target_sample_count / self.batch_size) * self.batch_size
@@ -160,8 +180,8 @@ class ParserTfds(Parser):
         if not self.is_training and self.dist_num_replicas and 0 < sample_count < target_sample_count:
             # Validation batch padding only done for distributed training where results are reduced across nodes.
             # For single process case, it won't matter if workers return different batch sizes.
-            # FIXME this needs more testing, possible for sharding / split api to cause differences of > 1?
+            # FIXME if using input_context or % based subsplits, sample count can vary by more than +/- 1 and this
-            assert target_sample_count - sample_count == 1  # should only be off by 1 or sharding is not optimal
+            # approach is not optimal
             yield img, sample['label']  # yield prev sample again
             sample_count += 1
 
@@ -176,7 +196,7 @@ class ParserTfds(Parser):
     def __len__(self):
         # this is just an estimate and does not factor in extra samples added to pad batches based on
         # complete worker & replica info (not available until init in dataloader).
-        return math.ceil(self.num_samples / self.dist_num_replicas)
+        return math.ceil(max(1, self.repeats) * self.num_samples / self.dist_num_replicas)
 
     def _filename(self, index, basename=False, absolute=False):
         assert False, "Not supported" # no random access to samples

timm/models/__init__.py

@@ -29,6 +29,7 @@ from .tnt import *
 from .tresnet import *
 from .vgg import *
 from .vision_transformer import *
+from .vision_transformer_hybrid import *
 from .vovnet import *
 from .xception import *
 from .xception_aligned import *

timm/models/layers/__init__.py

@@ -31,4 +31,4 @@ from .split_attn import SplitAttnConv2d
 from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
 from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
 from .test_time_pool import TestTimePoolHead, apply_test_time_pool
-from .weight_init import trunc_normal_
+from .weight_init import trunc_normal_, variance_scaling_, lecun_normal_

timm/models/layers/weight_init.py

@@ -2,6 +2,8 @@ import torch
 import math
 import warnings
 
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
 
 def _no_grad_trunc_normal_(tensor, mean, std, a, b):
     # Cut & paste from PyTorch official master until it's in a few official releases - RW
@@ -58,3 +60,30 @@ def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
         >>> nn.init.trunc_normal_(w)
     """
     return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == 'fan_in':
+        denom = fan_in
+    elif mode == 'fan_out':
+        denom = fan_out
+    elif mode == 'fan_avg':
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
+    elif distribution == "normal":
+        tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')

timm/models/resnetv2.py

@@ -274,7 +274,9 @@ class ResNetStage(nn.Module):
         return x
 
 
-def create_stem(in_chs, out_chs, stem_type='', preact=True, conv_layer=None, norm_layer=None):
+def create_resnetv2_stem(
+        in_chs, out_chs=64, stem_type='', preact=True,
+        conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32)):
     stem = OrderedDict()
     assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same')
 
@@ -322,7 +324,8 @@ class ResNetV2(nn.Module):
 
         self.feature_info = []
         stem_chs = make_div(stem_chs * wf)
-        self.stem = create_stem(in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
+        self.stem = create_resnetv2_stem(
+            in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
         stem_feat = ('stem.conv3' if 'deep' in stem_type else 'stem.conv') if preact else 'stem.norm'
         self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=stem_feat))
 

timm/models/vision_transformer.py

@@ -29,9 +29,7 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg, overlay_external_default_cfg
-from .layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
+from .layers import DropPath, to_2tuple, trunc_normal_, lecun_normal_
-from .resnet import resnet26d, resnet50d
-from .resnetv2 import ResNetV2
 from .registry import register_model
 
 _logger = logging.getLogger(__name__)
@@ -98,25 +96,21 @@ default_cfgs = {
         hf_hub='timm/vit_huge_patch14_224_in21k',
         num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
 
-    # hybrid models (weights ported from official Google JAX impl)
-    'vit_base_resnet50_224_in21k': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
-        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),
-    'vit_base_resnet50_384': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
-        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),
-
-    # hybrid models (my experiments)
-    'vit_small_resnet26d_224': _cfg(),
-    'vit_small_resnet50d_s3_224': _cfg(),
-    'vit_base_resnet26d_224': _cfg(),
-    'vit_base_resnet50d_224': _cfg(),
-
     # deit models (FB weights)
     'vit_deit_tiny_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
+    'vit_deit_tiny_patch16_224_in21k': _cfg(num_classes=21843),
+    'vit_deit_tiny_patch16_384': _cfg(input_size=(3, 384, 384)),
+
     'vit_deit_small_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'),
+    'vit_deit_small_patch16_224_in21k': _cfg(num_classes=21843),
+    'vit_deit_small_patch16_384': _cfg(input_size=(3, 384, 384)),
+
+    'vit_deit_small_patch32_224': _cfg(),
+    'vit_deit_small_patch32_224_in21k': _cfg(num_classes=21843),
+    'vit_deit_small_patch32_384': _cfg(input_size=(3, 384, 384)),
+
     'vit_deit_base_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',),
     'vit_deit_base_patch16_384': _cfg(
@@ -161,7 +155,6 @@ class Attention(nn.Module):
         super().__init__()
         self.num_heads = num_heads
         head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
         self.scale = qk_scale or head_dim ** -0.5
 
         self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
@@ -231,17 +224,17 @@ class HybridEmbed(nn.Module):
     """ CNN Feature Map Embedding
     Extract feature map from CNN, flatten, project to embedding dim.
     """
-    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
+    def __init__(self, backbone, img_size=224, patch_size=1, feature_size=None, in_chans=3, embed_dim=768):
         super().__init__()
         assert isinstance(backbone, nn.Module)
         img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
         self.img_size = img_size
+        self.patch_size = patch_size
         self.backbone = backbone
         if feature_size is None:
             with torch.no_grad():
-                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
+                # NOTE Most reliable way of determining output dims is to run forward pass
-                # map for all networks, the feature metadata has reliable channel and stride info, but using
-                # stride to calc feature dim requires info about padding of each stage that isn't captured.
                 training = backbone.training
                 if training:
                     backbone.eval()
@@ -257,8 +250,9 @@ class HybridEmbed(nn.Module):
                 feature_dim = self.backbone.feature_info.channels()[-1]
             else:
                 feature_dim = self.backbone.num_features
-        self.num_patches = feature_size[0] * feature_size[1]
+        assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
-        self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
+        self.num_patches = feature_size[0] // patch_size[0] * feature_size[1] // patch_size[1]
+        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size)
 
     def forward(self, x):
         x = self.backbone(x)
@@ -277,10 +271,11 @@ class VisionTransformer(nn.Module):
     Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
         - https://arxiv.org/abs/2012.12877
     """
+
     def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                  num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, distilled=False,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None,
-                 weight_init=''):
+                 act_layer=None, weight_init=''):
         """
         Args:
             img_size (int, tuple): input image size
@@ -307,10 +302,12 @@ class VisionTransformer(nn.Module):
         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
         self.num_tokens = 2 if distilled else 1
         norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        act_layer = act_layer or nn.GELU
+        patch_size = patch_size or (1 if hybrid_backbone is not None else 16)
 
         if hybrid_backbone is not None:
             self.patch_embed = HybridEmbed(
-                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
+                hybrid_backbone, img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
         else:
             self.patch_embed = PatchEmbed(
                 img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
@@ -325,7 +322,7 @@ class VisionTransformer(nn.Module):
         self.blocks = nn.Sequential(*[
             Block(
                 dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
             for i in range(depth)])
         self.norm = norm_layer(embed_dim)
 
@@ -344,20 +341,44 @@ class VisionTransformer(nn.Module):
         self.head_dist = nn.Linear(self.embed_dim, self.num_classes) \
             if num_classes > 0 and distilled else nn.Identity()
 
+        # Weight init
+        assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
+        head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
         trunc_normal_(self.pos_embed, std=.02)
-        trunc_normal_(self.cls_token, std=.02)
+        if weight_init.startswith('jax'):
-        if self.dist_token is not None:
+            # leave cls token as zeros to match jax impl
-            trunc_normal_(self.dist_token, std=.02)
+            for n, m in self.named_modules():
-        self.apply(self._init_weights)
+                _init_weights_jax(m, n, head_bias=head_bias)
+        else:
+            trunc_normal_(self.cls_token, std=.02)
+            if self.dist_token is not None:
+                trunc_normal_(self.dist_token, std=.02)
+            for n, m in self.named_modules():
+                self._init_weights(m, n, head_bias=head_bias)
 
-    def _init_weights(self, m):
+    def _init_weights(self, m, n: str = '', head_bias: float = 0., init_conv=False):
+        # This impl does not exactly match the official JAX version.
+        # When called w/o n, head_bias, init_conv args it will behave exactly the same
+        # as my original init for compatibility with downstream use cases (ie DeiT).
         if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
+            if n.startswith('head'):
-            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.zeros_(m.weight)
-                nn.init.constant_(m.bias, 0)
+                nn.init.constant_(m.bias, head_bias)
+            elif n.startswith('pre_logits'):
+                lecun_normal_(m.weight)
+                nn.init.zeros_(m.bias)
+            else:
+                trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+        elif init_conv and isinstance(m, nn.Conv2d):
+            # NOTE conv was left to pytorch default init originally
+            lecun_normal_(m.weight)
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
         elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
+            nn.init.zeros_(m.bias)
-            nn.init.constant_(m.weight, 1.0)
+            nn.init.ones_(m.weight)
 
     @torch.jit.ignore
     def no_weight_decay(self):
@@ -404,6 +425,32 @@ class VisionTransformer(nn.Module):
         return x
 
 
+def _init_weights_jax(m: nn.Module, n: str, head_bias: float = 0.):
+    # A weight init scheme closer to the official JAX impl than my original init
+    # NOTE: requires module name so cannot be used via module.apply()
+    if isinstance(m, nn.Linear):
+        if n.startswith('head'):
+            nn.init.zeros_(m.weight)
+            nn.init.constant_(m.bias, head_bias)
+        elif n.startswith('pre_logits'):
+            lecun_normal_(m.weight)
+            nn.init.zeros_(m.bias)
+        else:
+            nn.init.xavier_uniform_(m.weight)
+            if m.bias is not None:
+                if 'mlp' in n:
+                    nn.init.normal_(m.bias, 0, 1e-6)
+                else:
+                    nn.init.zeros_(m.bias)
+    elif isinstance(m, nn.Conv2d):
+        lecun_normal_(m.weight)
+        if m.bias is not None:
+            nn.init.zeros_(m.bias)
+    elif isinstance(m, nn.LayerNorm):
+        nn.init.zeros_(m.bias)
+        nn.init.ones_(m.weight)
+
+
 def resize_pos_embed(posemb, posemb_new, num_tokens=1):
     # Rescale the grid of position embeddings when loading from state_dict. Adapted from
     # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
@@ -411,7 +458,7 @@ def resize_pos_embed(posemb, posemb_new, num_tokens=1):
     ntok_new = posemb_new.shape[1]
     if num_tokens:
         posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
-        ntok_new -= 1
+        ntok_new -= num_tokens
     else:
         posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
     gs_old = int(math.sqrt(len(posemb_grid)))
@@ -474,7 +521,11 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs):
 
 @register_model
 def vit_small_patch16_224(pretrained=False, **kwargs):
-    """ My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3."""
+    """ My custom 'small' ViT model. embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.
+    NOTE:
+        * this differs from the DeiT based 'small' definitions with embed_dim=384, depth=12, num_heads=6
+        * this model does not have a bias for QKV (unlike the official ViT and DeiT models)
+    """
     model_kwargs = dict(
         patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,
         qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)
@@ -619,76 +670,6 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
     return model
 
 
-@register_model
-def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
-    """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    """
-    # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
-    backbone = ResNetV2(
-        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
-        preact=False, stem_type='same', conv_layer=StdConv2dSame)
-    model_kwargs = dict(
-        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone,
-        representation_size=768, **kwargs)
-    model = _create_vision_transformer('vit_base_resnet50_224_in21k', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_resnet50_384(pretrained=False, **kwargs):
-    """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
-    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
-    """
-    # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
-    backbone = ResNetV2(
-        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
-        preact=False, stem_type='same', conv_layer=StdConv2dSame)
-    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_base_resnet50_384', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_resnet26d_224(pretrained=False, **kwargs):
-    """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
-    """
-    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
-    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
-    """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
-    """
-    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
-    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_small_resnet50d_s3_224', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_resnet26d_224(pretrained=False, **kwargs):
-    """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
-    """
-    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
-    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_resnet50d_224(pretrained=False, **kwargs):
-    """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
-    """
-    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
-    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
-    model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs)
-    return model
-
-
 @register_model
 def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
     """ DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
@@ -699,6 +680,22 @@ def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_deit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
+    """ DeiT-tiny model"""
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, representation_size=192, **kwargs)
+    model = _create_vision_transformer('vit_deit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_tiny_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-tiny model"""
+    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+    model = _create_vision_transformer('vit_deit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_deit_small_patch16_224(pretrained=False, **kwargs):
     """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
@@ -709,6 +706,48 @@ def vit_deit_small_patch16_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_deit_small_patch16_224_in21k(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch32_224(pretrained=False, **kwargs):
+    """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch32_224_in21k(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, representation_size=384, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_deit_small_patch32_384(pretrained=False, **kwargs):
+    """ DeiT-small """
+    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_deit_small_patch32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_deit_base_patch16_224(pretrained=False, **kwargs):
     """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).

timm/models/vision_transformer_hybrid.py

@@ -0,0 +1,353 @@
+""" Hybrid Vision Transformer (ViT) in PyTorch
+
+A PyTorch implement of the Hybrid Vision Transformers as described in
+'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
+    - https://arxiv.org/abs/2010.11929
+
+NOTE This relies on code in vision_transformer.py. The hybrid model definitions were moved here to
+keep file sizes sane.
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .layers import StdConv2dSame, StdConv2d, to_2tuple
+from .resnet import resnet26d, resnet50d
+from .resnetv2 import ResNetV2, create_resnetv2_stem
+from .registry import register_model
+from timm.models.vision_transformer import _create_vision_transformer
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic',
+        'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
+        'first_conv': 'patch_embed.backbone.stem.conv', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    # hybrid in-21k models (weights ported from official Google JAX impl where they exist)
+    'vit_base_r50_s16_224_in21k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
+        num_classes=21843, crop_pct=0.9),
+
+    # hybrid in-1k models (weights ported from official JAX impl)
+    'vit_base_r50_s16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    # hybrid in-1k models (mostly untrained, experimental configs w/ resnetv2 stdconv backbones)
+    'vit_tiny_r_s16_p8_224': _cfg(),
+    'vit_tiny_r_s16_p8_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_small_r_s16_p8_224': _cfg(
+        crop_pct=1.0),
+    'vit_small_r_s16_p8_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_small_r20_s16_p2_224': _cfg(),
+    'vit_small_r20_s16_p2_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_small_r20_s16_224': _cfg(),
+    'vit_small_r20_s16_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_small_r26_s32_224': _cfg(),
+    'vit_small_r26_s32_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_base_r20_s16_224': _cfg(),
+    'vit_base_r20_s16_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_base_r26_s32_224': _cfg(),
+    'vit_base_r26_s32_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    'vit_base_r50_s16_224': _cfg(),
+
+    'vit_large_r50_s32_224': _cfg(),
+    'vit_large_r50_s32_384': _cfg(
+        input_size=(3, 384, 384), crop_pct=1.0),
+
+    # hybrid models (using timm resnet backbones)
+    'vit_small_resnet26d_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    'vit_small_resnet50d_s16_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    'vit_base_resnet26d_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    'vit_base_resnet50d_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+}
+
+
+def _resnetv2(layers=(3, 4, 9), **kwargs):
+    """ ResNet-V2 backbone helper"""
+    padding_same = kwargs.get('padding_same', True)
+    if padding_same:
+        stem_type = 'same'
+        conv_layer = StdConv2dSame
+    else:
+        stem_type = ''
+        conv_layer = StdConv2d
+    if len(layers):
+        backbone = ResNetV2(
+            layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
+            preact=False, stem_type=stem_type, conv_layer=conv_layer)
+    else:
+        backbone = create_resnetv2_stem(
+            kwargs.get('in_chans', 3), stem_type=stem_type, preact=False, conv_layer=conv_layer)
+    return backbone
+
+
+@register_model
+def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
+    """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    """
+    backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, representation_size=768, **kwargs)
+    model = _create_vision_transformer('vit_base_r50_s16_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r50_s16_384(pretrained=False, **kwargs):
+    """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+    """
+    backbone = _resnetv2((3, 4, 9), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r50_s16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
+    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_tiny_r_s16_p8_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs):
+    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=192, depth=12, num_heads=3, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_tiny_r_s16_p8_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r_s16_p8_224(pretrained=False, **kwargs):
+    """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r_s16_p8_224', pretrained=pretrained, **model_kwargs)
+
+    return model
+
+
+@register_model
+def vit_small_r_s16_p8_384(pretrained=False, **kwargs):
+    """ R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2(layers=(), **kwargs)
+    model_kwargs = dict(
+        patch_size=8, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r_s16_p8_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_p2_224(pretrained=False, **kwargs):
+    """ R52+ViT-S/S16 w/ 2x2 patch hybrid @ 224 x 224.
+    """
+    backbone = _resnetv2((2, 4), **kwargs)
+    model_kwargs = dict(
+        patch_size=2, embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_p2_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_p2_384(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 w/ 2x2 Patch hybrid @ 384x384.
+    """
+    backbone = _resnetv2((2, 4), **kwargs)
+    model_kwargs = dict(
+        embed_dim=384, patch_size=2, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_p2_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_224(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r20_s16_384(pretrained=False, **kwargs):
+    """ R20+ViT-S/S16 hybrid @ 384x384.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r20_s16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r26_s32_224(pretrained=False, **kwargs):
+    """ R26+ViT-S/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r26_s32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_r26_s32_384(pretrained=False, **kwargs):
+    """ R26+ViT-S/S32 hybrid @ 384x384.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_r26_s32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r20_s16_224(pretrained=False, **kwargs):
+    """ R20+ViT-B/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r20_s16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r20_s16_384(pretrained=False, **kwargs):
+    """ R20+ViT-B/S16 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r20_s16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r26_s32_224(pretrained=False, **kwargs):
+    """ R26+ViT-B/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r26_s32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r26_s32_384(pretrained=False, **kwargs):
+    """ R26+ViT-B/S32 hybrid.
+    """
+    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r26_s32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_r50_s16_224(pretrained=False, **kwargs):
+    """ R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
+    """
+    backbone = _resnetv2((3, 4, 9), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_r50_s16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_large_r50_s32_224(pretrained=False, **kwargs):
+    """ R50+ViT-L/S32 hybrid.
+    """
+    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_large_r50_s32_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_large_r50_s32_224_in21k(pretrained=False, **kwargs):
+    """ R50+ViT-L/S32 hybrid.
+    """
+    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
+    model_kwargs = dict(
+        embed_dim=768, depth=12, num_heads=12, representation_size=768, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_large_r50_s32_224_in21k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_large_r50_s32_384(pretrained=False, **kwargs):
+    """ R50+ViT-L/S32 hybrid.
+    """
+    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_large_r50_s32_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_resnet26d_224(pretrained=False, **kwargs):
+    """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
+    """
+    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
+    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_resnet50d_s16_224(pretrained=False, **kwargs):
+    """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
+    """
+    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
+    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_small_resnet50d_s16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_resnet26d_224(pretrained=False, **kwargs):
+    """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
+    """
+    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_resnet50d_224(pretrained=False, **kwargs):
+    """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
+    """
+    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
+    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+    model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs)
+    return model

timm/optim/__init__.py

@@ -10,4 +10,4 @@ from .radam import RAdam
 from .rmsprop_tf import RMSpropTF
 from .sgdp import SGDP
 
-from .optim_factory import create_optimizer
+from .optim_factory import create_optimizer, optimizer_kwargs

timm/optim/optim_factory.py

@@ -1,8 +1,11 @@
 """ Optimizer Factory w/ Custom Weight Decay
 Hacked together by / Copyright 2020 Ross Wightman
 """
+from typing import Optional
+
 import torch
-from torch import optim as optim
+import torch.nn as nn
+import torch.optim as optim
 
 from .adafactor import Adafactor
 from .adahessian import Adahessian
@@ -37,9 +40,49 @@ def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
         {'params': decay, 'weight_decay': weight_decay}]
 
 
-def create_optimizer(args, model, filter_bias_and_bn=True):
+def optimizer_kwargs(cfg):
-    opt_lower = args.opt.lower()
+    """ cfg/argparse to kwargs helper
-    weight_decay = args.weight_decay
+    Convert optimizer args in argparse args or cfg like object to keyword args for updated create fn.
+    """
+    kwargs = dict(opt_name=cfg.opt, lr=cfg.lr, weight_decay=cfg.weight_decay)
+    if getattr(cfg, 'opt_eps', None) is not None:
+        kwargs['eps'] = cfg.opt_eps
+    if getattr(cfg, 'opt_betas', None) is not None:
+        kwargs['betas'] = cfg.opt_betas
+    if getattr(cfg, 'opt_args', None) is not None:
+        kwargs.update(cfg.opt_args)
+    kwargs['momentum'] = cfg.momentum
+    return kwargs
+
+
+def create_optimizer(
+        model: nn.Module,
+        opt_name: str = 'sgd',
+        lr: Optional[float] = None,
+        weight_decay: float = 0.,
+        momentum: float = 0.9,
+        filter_bias_and_bn: bool = True,
+        **kwargs):
+    """ Create an optimizer.
+
+    TODO currently the model is passed in and all parameters are selected for optimization.
+    For more general use an interface that allows selection of parameters to optimize and lr groups, one of:
+      * a filter fn interface that further breaks params into groups in a weight_decay compatible fashion
+      * expose the parameters interface and leave it up to caller
+
+    Args:
+        model (nn.Module): model containing parameters to optimize
+        opt_name: name of optimizer to create
+        lr: initial learning rate
+        weight_decay: weight decay to apply in optimizer
+        momentum:  momentum for momentum based optimizers (others may use betas via kwargs)
+        filter_bias_and_bn:  filter out bias, bn and other 1d params from weight decay
+        **kwargs: extra optimizer specific kwargs to pass through
+
+    Returns:
+        Optimizer
+    """
+    opt_lower = opt_name.lower()
     if weight_decay and filter_bias_and_bn:
         skip = {}
         if hasattr(model, 'no_weight_decay'):
@@ -48,26 +91,18 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
         weight_decay = 0.
     else:
         parameters = model.parameters()
-
     if 'fused' in opt_lower:
         assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
 
-    opt_args = dict(lr=args.lr, weight_decay=weight_decay)
+    opt_args = dict(lr=lr, weight_decay=weight_decay, **kwargs)
-    if hasattr(args, 'opt_eps') and args.opt_eps is not None:
-        opt_args['eps'] = args.opt_eps
-    if hasattr(args, 'opt_betas') and args.opt_betas is not None:
-        opt_args['betas'] = args.opt_betas
-    if hasattr(args, 'opt_args') and args.opt_args is not None:
-        opt_args.update(args.opt_args)
-
     opt_split = opt_lower.split('_')
     opt_lower = opt_split[-1]
     if opt_lower == 'sgd' or opt_lower == 'nesterov':
         opt_args.pop('eps', None)
-        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+        optimizer = optim.SGD(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'momentum':
         opt_args.pop('eps', None)
-        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
+        optimizer = optim.SGD(parameters, momentum=momentum, nesterov=False, **opt_args)
     elif opt_lower == 'adam':
         optimizer = optim.Adam(parameters, **opt_args)
     elif opt_lower == 'adamw':
@@ -79,29 +114,29 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
     elif opt_lower == 'adamp':        
         optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
     elif opt_lower == 'sgdp':
-        optimizer = SGDP(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+        optimizer = SGDP(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'adadelta':
         optimizer = optim.Adadelta(parameters, **opt_args)
     elif opt_lower == 'adafactor':
-        if not args.lr:
+        if not lr:
             opt_args['lr'] = None
         optimizer = Adafactor(parameters, **opt_args)
     elif opt_lower == 'adahessian':
         optimizer = Adahessian(parameters, **opt_args)
     elif opt_lower == 'rmsprop':
-        optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
+        optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=momentum, **opt_args)
     elif opt_lower == 'rmsproptf':
-        optimizer = RMSpropTF(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
+        optimizer = RMSpropTF(parameters, alpha=0.9, momentum=momentum, **opt_args)
     elif opt_lower == 'novograd':
         optimizer = NovoGrad(parameters, **opt_args)
     elif opt_lower == 'nvnovograd':
         optimizer = NvNovoGrad(parameters, **opt_args)
     elif opt_lower == 'fusedsgd':
         opt_args.pop('eps', None)
-        optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+        optimizer = FusedSGD(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'fusedmomentum':
         opt_args.pop('eps', None)
-        optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
+        optimizer = FusedSGD(parameters, momentum=momentum, nesterov=False, **opt_args)
     elif opt_lower == 'fusedadam':
         optimizer = FusedAdam(parameters, adam_w_mode=False, **opt_args)
     elif opt_lower == 'fusedadamw':

train.py

@@ -33,7 +33,7 @@ from timm.models import create_model, safe_model_name, resume_checkpoint, load_c
     convert_splitbn_model, model_parameters
 from timm.utils import *
 from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
-from timm.optim import create_optimizer
+from timm.optim import create_optimizer, optimizer_kwargs
 from timm.scheduler import create_scheduler
 from timm.utils import ApexScaler, NativeScaler
 
@@ -142,6 +142,8 @@ parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                     help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
 parser.add_argument('--epochs', type=int, default=200, metavar='N',
                     help='number of epochs to train (default: 2)')
+parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
+                    help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
 parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                     help='manual epoch number (useful on restarts)')
 parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
@@ -258,6 +260,8 @@ parser.add_argument('--no-prefetcher', action='store_true', default=False,
                     help='disable fast prefetcher')
 parser.add_argument('--output', default='', type=str, metavar='PATH',
                     help='path to output folder (default: none, current dir)')
+parser.add_argument('--experiment', default='', type=str, metavar='NAME',
+                    help='name of train experiment, name of sub-folder for output')
 parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                     help='Best metric (default: "top1"')
 parser.add_argument('--tta', type=int, default=0, metavar='N',
@@ -385,7 +389,7 @@ def main():
         assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
         model = torch.jit.script(model)
 
-    optimizer = create_optimizer(args, model)
+    optimizer = create_optimizer(model, **optimizer_kwargs(cfg=args))
 
     # setup automatic mixed-precision (AMP) loss scaling and op casting
     amp_autocast = suppress  # do nothing
@@ -451,7 +455,9 @@ def main():
 
     # create the train and eval datasets
     dataset_train = create_dataset(
-        args.dataset, root=args.data_dir, split=args.train_split, is_training=True, batch_size=args.batch_size)
+        args.dataset,
+        root=args.data_dir, split=args.train_split, is_training=True,
+        batch_size=args.batch_size, repeats=args.epoch_repeats)
     dataset_eval = create_dataset(
         args.dataset, root=args.data_dir, split=args.val_split, is_training=False, batch_size=args.batch_size)
 
@@ -541,13 +547,15 @@ def main():
     saver = None
     output_dir = ''
     if args.local_rank == 0:
-        output_base = args.output if args.output else './output'
+        if args.experiment:
-        exp_name = '-'.join([
+            exp_name = args.experiment
-            datetime.now().strftime("%Y%m%d-%H%M%S"),
+        else:
-            safe_model_name(args.model),
+            exp_name = '-'.join([
-            str(data_config['input_size'][-1])
+                datetime.now().strftime("%Y%m%d-%H%M%S"),
-        ])
+                args.model,
-        output_dir = get_outdir(output_base, 'train', exp_name)
+                str(data_config['input_size'][-1])
+            ])
+        output_dir = get_outdir(args.output if args.output else './output/train', exp_name)
         decreasing = True if eval_metric == 'loss' else False
         saver = CheckpointSaver(
             model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,

validate.py

@@ -152,7 +152,7 @@ def validate(args):
     param_count = sum([m.numel() for m in model.parameters()])
     _logger.info('Model %s created, param count: %d' % (args.model, param_count))
 
-    data_config = resolve_data_config(vars(args), model=model, use_test_size=True)
+    data_config = resolve_data_config(vars(args), model=model, use_test_size=True, verbose=True)
     test_time_pool = False
     if not args.no_test_pool:
         model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True)